Passenger Demand Forecasting in Railways using Machine Learning

Abstract

This review examines recent advancements in machine learning (ML) and deep learning (DL) techniques for demand forecasting across diverse domains, including railway passenger flow prediction, business intelligence, and student academic performance. The study compares and contrasts various methodologies, such as hybrid deep learning approaches, graph-based learning frameworks, and multiclass prediction models, highlighting their strengths and limitations. Key innovations include the integration of spatial-temporal features, handling of imbalanced datasets, and incorporation of capacity constraints. The comparative analysis reveals that while ML and DL models achieve high accuracy in specific domains, challenges remain in terms of data quality, model generalization, scalability, and interpretability. The review also identifies future research directions, such as the creation of benchmark datasets, development of hybrid and interpretable models, domain adaptation and transfer learning, real-time and scalable implementation, uncertainty and robustness modeling, and ethical considerations. Overall, this study provides valuable insights into the current state and future potential of ML and DL techniques for demand forecasting, emphasizing the need for cross-domain collaboration and the development of more reliable, transparent, and widely applicable solutions.